{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 9.23207745]\n",
      " [11.182937  ]\n",
      " [13.13379655]]\n",
      "w: [[1.95085955]]\n",
      "b: [1.42863925]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import  matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import datasets, linear_model\n",
    "\n",
    "\n",
    "x = np.linspace(0, 10, 100)\n",
    "y = 2 * x + 1 + np.random.randn(100)\n",
    "\n",
    "x=[[i] for i in x]\n",
    "y=[[i] for i in y]\n",
    "\n",
    "model = linear_model.LinearRegression()\n",
    "model.fit(x, y)\n",
    "\n",
    "x_=[[4],[5],[6]]\n",
    "y_=model.predict(x_)\n",
    "print(y_)\n",
    "\n",
    "print(\"w:\",model.coef_)\n",
    "print(\"b:\",model.intercept_)\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.plot(x_, y_, color='red', linewidth=3)\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
